搜索资源列表
yi_chuan_suan_fa
- 遗传算法的小程序，解决jobshop问题的-The little procedure of the genetic algorithm, ones that solves jobshop problem
tenlei
- function [U,center,result,w,obj_fcn]= fenlei(data) [data_n,in_n] = size(data) m= 2 ％ Exponent for U max_iter = 100 ％ Max. iteration min_impro =1e-5 ％ Min. improvement c=3 [center, U, obj_fcn] = fcm(data, c) for i=1:max_iter if
EvaluateMetric
- Clustering Evaluation: Evaluate the clustering result by accuracy and normalized mutual information Deng Cai, Xiaofei He, and Jiawei Han, "Document Clustering Using Locality Preserving Indexing", in IEEE TKDE, 2005. Bibtex source bestMap hun
TEC_CLPSO-Sept-05
- TEC_test.m and TEC_test_rot.m are the main functions. TEC_test.m is for the unrotated problem and TEC_test_rot.m is for the rotated ones. TEC_test_function.m includes the benchmark functions, and the data files are the corrsponding data for
SIS2005-function-codes
- TEC_test.m and TEC_test_rot.m are the main functions. TEC_test.m is for the unrotated problem and TEC_test_rot.m is for the rotated ones. TEC_test_function.m includes the benchmark functions, and the data files are the corrsponding data for
DMS-PSOPHS--Exp-S-W-Applns
- TEC_test.m and TEC_test_rot.m are the main functions. TEC_test.m is for the unrotated problem and TEC_test_rot.m is for the rotated ones. TEC_test_function.m includes the benchmark functions, and the data files are the corrsponding data for
DMS_PSO_func
- TEC_test.m and TEC_test_rot.m are the main functions. TEC_test.m is for the unrotated problem and TEC_test_rot.m is for the rotated ones. TEC_test_function.m includes the benchmark functions, and the data files are the corrsponding data for
2LB-MOPSOsourcecodes
- TEC_test.m and TEC_test_rot.m are the main functions. TEC_test.m is for the unrotated problem and TEC_test_rot.m is for the rotated ones. TEC_test_function.m includes the benchmark functions, and the data files are the corrsponding data for
Multi-class-SVM-Image-Classification
- 基于神经网络的遥感图像分类取得了较好的效果,但存在固有的过学习、易陷入局部极小等缺点.支持向量机机器学习方法,根据结构风险最小化(SRM)原理,表现出很多优于其他传统方法的性能,本研究的基于多类支持向量机分类器的遥感图像分类取得了达95.4 的分类精度.但由于遥感图像分类类别多,所需训练样本较大,人工选择效率较低,为此提出以人工选择初始聚类质心、C均值模糊聚类算法自动标注训练样本的基于多类支持向量机的半监督式遥感图像分类方法,期望能在获得适用的分类精度的基础上有效提高分类效率-Neural ne
SceneTextCNN_demo.tar
- 端至端卷积神经网络的文字识别,代码演示包. 它包含我们的论文中使用的所有主要组成部分: kmeans无监督特征学习 + 卷积神经网络(CNN)-This is a demo package of the code we used for our paper, "End-to-End Text Recognition with Convolutional Neural Networks", T. Wang, D. Wu, A. Coates, A. Ng, in ICPR 2012.
detection-and-tracking
- 雷达检测跟踪过程无缝连接过程。 雷达检测跟踪过程无缝连接过程。-Detection and tracking are normally considered separate processes. First the signal processing system associated with a sensor examines the signal to determine whether to call detection. Once detection is called, it is
neural-network-genetic-algorithm-master
- 为了自动学习CNN的深度网络结构，网络结构的数量随着网络中间层数量的增加呈指数增长，这启发我们使用遗传算法有效地遍历这个大的搜索空间。我们首先提出一种编码方法，将每个网络结构表示为一个固定长度的二进制字符串，然后通过生成一组随机个体来初始化遗传算法。在每一代中，我们定义标准的遗传操作(如选择、突变和交叉)来消除弱势个体并产生更具竞争力的个体。(In order to automatically learn the deep network structure of CNN, the number